The Changing Landscape Of Data Visualization Requires A Radical New Approach
In a recent media interview I was asked about whether the requirements for data visualization had changed. The questions were focused around whether users are still satisfied with dashboards, graphs and charts or do they have new needs, demands and expectations.
Arguably, Ancient Egyptian hieroglyphics were probably the first real "commercial" examples of data visualization (though many people before the Egyptians also used the same approach — but more often as a general communications tool). Since then, visualization of data has certainly always been both a popular and important topic. For example, Florence Nightingale changed the course of healthcare with a single compelling polar area chart on the causes of death during the Crimean War.
In looking at this question of how and why data visualization might be changing, I identified at least 5 major triggers. Namely:
- Increasing volumes of data. It's no surprise that we now have to process much larger volumes of data. But this also impacts the ways we need to represent it. The volume of data stimulates new forms of visualization tools. While not all of these tools are new (strictly speaking), they have at least begun to find a much broader audience as we find the need to communicate much more information much more rapidly. Time walling and infographics are just two approaches that are not necessarily all that new but they have attracted much greater usage as a direct result of the increasing volume of data.
- More complexity in data relationships. More traditional computer-based data visualization approaches were fairly simplistic in their ability to represent the complex relationships within data. A chart based on an Excel spreadsheet for example, represented the data contained only within the spreadsheet's own rows and columns. With a greater emphasis on integrating data from a wide variety of information sources, new methods of visualization need to be used. These again include infographics but also interactive bubble charts, 3D data "landscapes" and semantic analysis maps.
- Interactivity. Users now expect that they can completely interact with data, not just visualize it. Static reports simply don't cut it anymore. This means that the visualization tools must understand the context of the data and be able to dynamically adapt the navigation, look and feel and even the core functionality as user manipulate and immerse themselves in the information.
- Gamification. As per the previous bullet point, static representations of data in charts and graphs no longer hold the attention of users. Interactivity does go part of the way. However, we're now seeing much more emphasis on introducing "game play" as part of data visualization. This can be as simple as including some level of interactivity to allow the user to drill down into information, but it can also go to the extreme of integrating real-time, multi-player simulations. These are common in highly regulated or life-threatening work environments where attention spans may mean the difference between life and death.
- Cognitive computing. Smart "thinking machines" appear to be becoming the norm. But unfortunately it is still just an illusion (yes, including IBM's Watson). Though our smartphones and tablets may know exactly where we are (by the inbuilt GPS location services) they can also know much richer information about what we're doing — using things like the information available in our calendars for example. Data visualization in this kind of context means that complex data can be represented in some new and compelling ways.
In my Google Nexus device, it uses my current location and the contents of my calendar to pop up an infographic panel. This infographic panel can contain the current time, alternative route maps, traffic loads on those routes, weather information and expected travel times to ensure that I make it comfortably to my next appointment in plenty of time. You could certainly make the argument that this is not necessarily a new form of visualization, but a collection of various existing data visualization approaches. And that's exactly my argument. While many people fail to see this scenario as data visualization, to me, it's a very good example of how this market really is changing. It's certainly no longer just about historic transactional charts and graphs that the user has to read and interpret. Or even drill down BI. The new approach to data virtualization is both dynamically generated and autonomically optimized. Most of all, it answers very complex questions with a simple graphical response. Where do I have to be next and how long will it take me to get there? Which route should I take and when do I need to leave to compensate for traffic? Very complex questions, but a very simple graphical response.